CoCoHD: Congress Committee Hearing Dataset
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910632358445056 |
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| author | Hiray, Arnav Liu, Yunsong Song, Mingxiao Shah, Agam Chava, Sudheer |
| author_facet | Hiray, Arnav Liu, Yunsong Song, Mingxiao Shah, Agam Chava, Sudheer |
| contents | U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the Congress Committee Hearing Dataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee's stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03099 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoCoHD: Congress Committee Hearing Dataset Hiray, Arnav Liu, Yunsong Song, Mingxiao Shah, Agam Chava, Sudheer Computation and Language U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the Congress Committee Hearing Dataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee's stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector. |
| title | CoCoHD: Congress Committee Hearing Dataset |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.03099 |